Atribución-NoComercial 4.0 InternacionalPérez Pozo, ÁlvaroRosa, Javier de laRos Muñoz, SalvadorGonzález Blanco, ElenaHernández Lorenzo, LauraSisto, Mirella de2024-09-022024-09-022022Pérez Pozo, Á., de la Rosa, J., Ros, S., González-Blanco, E., Hernández, L., & de Sisto, M. (2022). A bridge too far for artificial intelligence?: Automatic classification of stanzas in Spanish poetry. Journal of the Association for Information Science and Technology, 73(2), 258-267. https://doi.org/10.1002/ASI.245322330-1643https://doi.org/10.1002/asi.24532https://hdl.handle.net/20.500.14468/23593The registered version of this article, first published in "Journal of the Association for Information Science and Technology, 73(2), 258-267", is available online at the publisher's website: Wiley, https://doi.org/10.1002/asi.24532La versión registrada de este artículo, publicado por primera vez en "Journal of the Association for Information Science and Technology, 73(2), 258-267", está disponible en línea en el sitio web del editor: Wiley, https://doi.org/10.1002/asi.24532Funding information: H2020 European Research Council, Grant/Award Number: ERC-2015-STG-679528The rise in artificial intelligence and natural language processing techniques has increased considerably in the last few decades. Historically, the focus has been primarily on texts expressed in prose form, leaving mostly aside figurative or poetic expressions of language due to their rich semantics and syntactic complexity. The creation and analysis of poetry have been commonly carried out by hand, with a few computer-assisted approaches. In the Spanish context, the promise of machine learning is starting to pan out in specific tasks such as metrical annotation and syllabification. However, there is a task that remains unexplored and underdeveloped: stanza classification. This classification of the inner structures of verses in which a poem is built upon is an especially relevant task for poetry studies since it complements the structural information of a poem. In this work, we analyzed different computational approaches to stanza classification in the Spanish poetic tradition. These approaches show that this task continues to be hard for computers systems, both based on classical machine learning approaches as well as statistical language models and cannot compete with traditional computational paradigms based on the knowledge of experts.eninfo:eu-repo/semantics/openAccess62 Ciencias de las Artes y las Letras::6202 Teoría, análisis y crítica literariasartículo